Category Archives: Artificial Intelligence

3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021 – Channel Futures

Artificial intelligence and machine learning can help make ITSM processes more efficient.

CIOs are now using artificial intelligence (AI) and machine learning (ML) technologies to make IT service management processes more efficient.

A typical use case for artificial intelligence in ITSM involves natural language processing (NLP). User requests for IT services are automated using NLP. IT practitioners get a deeper understanding of their processes by applying machine learning (ML) to ITSM data. The natural language processing technology that powers virtual agents is very often integrated with channels that the employees are familiar with. Many organizations integrate virtual agents with chat services like Slack, where employees can directly communicate with the IT service desks.

ITSM systems generate large volumes of data, so applying machine learning to these systems makes sense. The data collected by these systems is large not only in volume but also in detail. All of this data helps us understand existing IT assets and processes, along with information about their ownership.

These insights help IT understand the real priorities of ITSM issues, work proactively instead of reactively, accelerate time to resolution and enhance employee productivity. In the current age of remote work, enhancing employee experience to ensure business continuity is at the top of every CIOs mind, and artificial intelligence will prove to be just the right technology to use to face this new challenge.

Lets look at the three ways artificial intelligence will help IT MSPs to do better in 2021.

Chatbots integrated with an ITSM environment can easily be used to categorize the problem in employee requests. For example, if an organization has integrated Freshservices Virtual Agent with MS Teams, it creates a channel for employees to raise a service request or resolve their issues. The chat interface is a familiar UI for the employees, and the chatbot will identify whether the employee has a service request or an incident to raise using machine learning.

Another important and time-consuming task normally performed by an agent or complex workflows is routing a ticket to the correct support groups. Chatbots will triage the incoming requests or incidents to the right support group, making the process a lot efficient.

The historical ticket data and an extensive ITSM knowledge base will help agents resolve various requests faster. However, this requires the admins/agents to create an extensive knowledge base covering a wide range of requests and incidents. The ability to directly convert a resolution email to a knowledge base article will help build a rich knowledge base repository. When a similar problem arises, AI and machine learning can be used to dig through this repository and present the closest match to resolve the issue faster.

A well-managed repository will also help with incident resolution throughout the solution. AI can provide advice that is as simple as a related or similar incident along with its history, or a solution article with words that match the current incident/request, thereby shortening the time taken to think through the issue from scratch.

Like employee onboarding, many requests to IT demand human staff hours to perform a series of complex tasks to fulfill the requests. Machine learning models watch and learn how humans carry on and execute them to automate them in the future. By recognizing patterns in the request types and execution methods, machine learning-based models make intelligent suggestions for even the most complex IT processes.

Hemalakshmi is a Product Expert with Freshworks. Her responsibility includes educating and helping industry peers and customers on best practices, tips and tricks, quick guides, and solutions around IT Service Management and its various use cases. In her 6+ years of experience in the core SaaS business applications serving as a product expert, Hema has worked with multiple businesses in helping them with their business needs and setting up their service desk solution Freshservice.Follow her onLinkedIn.

This guest blog is part of a Channel Futures sponsorship.

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3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021 - Channel Futures

Leap And Learn: The Common Thread Of Artificial Intelligence Success Stories – Forbes

AI success is built on learning

Enterprises seeing real success with artificial intelligence have something in common: they are capable of learning quickly from their successes or failures and re-applying those lessons into the mainstream of their businesses.

Of course, theres nothing new about the ability to rinse, learn and repeat, which has been a fundamental tenet of business success for ages. But because AI is all about real-time, nanosecond responsiveness to a range of things, from machines to markets, the ability to leap and learn at a blinding pace has taken on a new urgency.

At this moment, only 10% of companies are seeing financial benefits from their AI initiatives, a survey of 3,000 executives conducted by Boston Consulting Group and MIT Sloan Management Review finds. There is a lot of AI going around: more than half, 57%, piloting or deploying AI up from 46% in 2017. In addition, at least 70% understand the business value proposition of AI. But financial results have been elusive.

So, what are the enlightened 10% doing to finally realize actual, tangible gains from AI? They do all the right things, of course, but theres an extra piece of the magic thrown in. For instance, scaling AI seen as the path to enterprise adoption has only limited value by itself. Adding the ability to embed AI into processes and solutions improves the likelihood of significant benefits dramatically, but only to 39%, the survey shows.

Successful AI adopters have figured out how to learn from their AI experiences and apply them in forward-looking ways to their businesses, the survey reports authors, led by Sam Ransbotham, conclude. Our survey analysis demonstrates that leaders share one outstanding feature they intend to become more adept learners with AI. This ability to learn and understand the potential and pitfalls of AI enable them to sense and respond quickly and appropriately to changing conditions, such as a new competitor or a worldwide pandemic, are more likely to take advantage of those disruptions.

In other words, they give executives and employees the space they need to better understand, adjust and adapt to AI-driven processes and figure out their roles in making it all work. Automation is not thrust upon them with no preparation or training. Realizing significant financial benefits with AI requires far more than a foundation in data, infrastructure, and talent, the researchers state. Even embedding AI in business processes is not enough.

Those organizations that lead the way with AI success pursue the following strategies:

They facilitate systematic and continuous learning between humans and machines. Organizational learning with AI isnt just machines learning autonomously. Or humans teaching machines. Or machines teaching humans, Ransbotham and his co-authors state. Its all three. Organizations that enable humans and machines to continuously learn from each other with all three methods are five times more likely to realize significant financial benefits than organizations that learn with a single method.

They develop multiple ways for humans and machines to interact. Deploying the appropriate interaction modes in the appropriate context is critical, the co-authors state. For example, some situations may require an AI system to make a recommendation and humans to decide whether to implement it. Some context-rich environments may require humans to generate solutions and AI to evaluate the quality of those solutions.

They change to learn, and learn to change. Successful initiatives dont just change processes to use AI; they change processes in response to what they learn with AI.

AI has great potential to expand our visions of where and how businesses can deliver greater service in the months and years ahead. But it requires more than simply installing new systems and processes and waiting to see the results. Its a continuous process of improvement and innovation,

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Leap And Learn: The Common Thread Of Artificial Intelligence Success Stories - Forbes

Re-Humanizing Fundraising With Artificial Intelligence – Stanford Social Innovation Review

(Photo by iStock/xijian)

Conventional wisdom about nonprofit fundraising considers these two statements equally true: 1) Acquiring new donors loses money, and 2) Future gifts from new donors make up for the money lost on acquisition.

Alas, only one of them is true. Acquiring new donors does, indeed, lose money, often estimated at 50 percent of the initial gift. However, according to Blackbaud, fewer than a quarter of those initial donors will renew their gift. The math gets even worse in out-years, as 60 percent of donors lapse year after year.

The reality is that most organizations spend an enormous amount of time frantically trying to refill their leaky bucket of donors. The result is a transactional approach to fundraising that requires constantly asking for donations rather than spending time getting to know donors, particularly donors who arent writing huge checks. Just because its the norm, however, doesnt make it good or effective, particularly during a pandemic when everyone is distracted, scared, and stretched.

We recently released a report funded by the Bill and Melinda Gates Foundation on using artificial intelligence (AI) for fundraising and philanthropy. The report outlines ways that nonprofits are beginning to use AI to increase giving, and while the fact that the most powerful technology in history can help nonprofits raise more money didnt surprise us, we were surprised by how much opportunity nonprofits have to use AI to re-imagine and re-humanize fundraising.

AI automates tasks that previously only humans could do. The field isnt newits been around for decadesbut its recently become much less expensive, making it available for everyday use and by smaller organizations.

AI tools for increasing fundraising currently include:

One example of a nonprofit putting AI tools into action is the 24-hour fundraising marathon Extra Life, a fundraising effort of Childrens Miracle Network Hospitals. Staff members were getting overwhelmed answering the same question from Canadian supporters, who wanted to know what currency Extra Life would use to process their donations. To ameliorate this, Extra Life added a chatbot to its donation page specifically to answer this question, and even used an algorithm to personalize the landing page so that the chatbot appeared only for Canadian donors.

The chatbot on the Extra Life website provides instant answers to common donor questions about things like conversion rates.

Another example is the Cure Alzheimer's Fund, which raised $1.2 million in donations using Gravytys AI-powered fundraising software. Gravyty drafts emails to existing donors based on their preferences and previous actions, and highlights donors who are on the cusp of lapsing. Staff members review the emails and cultivation plan, then send them out the door. Gravyty isnt just automating renewal letters; by helping fundraisers continuously improve the specific content and timing of messages to individual donors, its adding more intelligence into the fundraising system.

Similarly, Rainforest Action Network piloted software from Accessible Intelligence Limited in May 2020. This software recommends the right content to include in fundraising appeals (including writing style, specific ask, and even subject lines to test), as well as the right number and interval of communication touch points. As a result, open rates and signed online petitions increased significantly. More importantly, conversion of one-time donors to monthly donors increased 866 percent. (That is not a typo!)

Since the publication of our report, weve been thinking about the time development staff could save by using AI. What could change? What could staff do differently or better with this precious gift of time? We see a great opportunity for development teams to patch the holes in the leaky bucket of fundraising, and enable their organizations to move from transactional to relational fundraising, starting with these three activities:

1.Add retention rates to dashboards and budgetary calculations. We have served on many boards and cant recall one discussion focused on donor retention. Organizations need to measure and monitor donor retention rates over time. They also need to calculate the net cost of fundraising, as well as the cost of money raised through acquisition and lost through lapsed donors over time.

2.Put time for conversations with donors, clients, and volunteers on the calendar. Activities that arent on the calendar dont get done. Staff and leading volunteers (such as board members) need to spend time listening to donors and stop treating them like ATMs. Instead, they need to find out why the cause is important to each donornot just major donors, but donors at every leveland what makes them feel good or bad when they give.

3.Establish ethical-use guidelines around the use of AI. Its critically important that organizations use the incredible power of AI with great care. We recommend establishing an outside committee of advisors to discuss issues such as the use and storage of data, the need to inform people when they are talking to a robot and not a person, and careful monitoring of AI-powered efforts for racial and other biases.

One person we interviewed for our report said, AI cant fix bad fundraising practices. Our greatest fear is that nonprofit leaders will use the incredible speed and power of AI to supersize existing transactional fundraising practices. We implore them to take the care and time needed to create a new chapter in fundraising, where every person can be heard and where most donors stay with causes for years, not months.

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Re-Humanizing Fundraising With Artificial Intelligence - Stanford Social Innovation Review

Artificial Intelligence Is Used To Understand The Geospatial World To Improve Business And Governmental Performance – Forbes

Artificial Intelligence

Recently, there was brief news about Microsoft Flight Simulator and a tower more than 200 stories tall created by a typo. As funny as that was, it missed the larger picture. Google Earth started a trend that has continued, and the virtualization of the world has proceeded at a rapid pace. It is now to the point where real business benefit is being gained by such work, supporting the application of artificial intelligence (AI) to even more problems.

There have been smaller discussions about virtualization and augmented reality, to analysis and improve performance in stores and other smaller spaces. However, similar to how a drive for better gaming led to NVIDIAs GPUs, which helped advance AI, the business of capturing a global image base to improve gaming can now help AI lend its skills to new areas.

Whats interesting is that the volume of geographic imaging is beginning to provide analytics to a wide range of businesses. Both businesses and governments are beginning to use the images to estimate forest conditions, crop yields, and other large scale issues. In another interesting application, analysis of buildings and other large structures is beginning to yield ROI on inspections.

One example is inspection of a type of structure called a floating oil tank. It is, as the name implies, an oil storage tank. Whats interesting is the roof floats on top of the oil, raising and lowering based on oil level. The Blackshark.ai system, which includes 200 GPUs, works with satellite imagery, the time stamp of the image, and the shadows provided by the facilities. It is then simple trigonometry to provide an estimate of oil volume. Note, this is something that is good for government oil reserve estimates and for insurance, but companies would want more detailed information.

In that example, the AI is in the computer vision component, it isnt required for the estimate creation. However, there are examples where AI can be used for additional analysis. Imagine a government trying to estimate energy usage or tax base depending on building type. A satellite image can be analyzed by an AI system which can identify building types by what is on the roof. The size of HVAC systems, for instance, can help to identify a buildings size and use type.

The image is the starting point for the analysis, said Michael Putz, CEO and Co-founder, Blackshark.ai. Semantic reconstruction is the process of adding semantic information needed for critical decisions by companies, governments, and individuals. Past computer vision systems have only enhanced images, leaving it to people to clarify items. Artificial intelligence can do the work of identifying objects, adding the semantics necessary to speed analysis and enhance the accuracy of decision making.

In the aftermath of events such as earthquakes, floods, and other natural disaster, comparisons to previous images can quickly prepare both governments, NGOs, and insurance companies in taking both faster and more effective action.

Rendering 2D images into 3D simulations also provide other business benefits. Consider wireless signal propagation. 3G and 5G have different broadcast features. Simulating geospatial features can aid coverage range and engineering cost analysis for optimal ROI for tower placement.

Notice the individuals mentioned Michael Putz. Think about semantic analysis and someones back yard. As AI is able to identify objects and even render 2D satellite images into 3D representations, that enhances the ability for homeowners and small businesses to work together to combine AI and VR to plan for changes. The example Mr. Putz provided was adding a pool to a yard. Being able to visualize that in 3D could help owners check line of site and see if other work, such as higher fences for privacy, might be needed.

At this point, I see the technology being focused on the higher end solutions, such as those for large companies and for government agencies. As with all new product arenas, advances will drive price down and the Cloud model will mean consumer applications will become profitable just not yet.

Geospatial image capture started off small, but has now grown to a massive scale. The addition of AI both improves computer vision and downstream analysis. This is another are where the world around us is being enhanced by artificial intelligence.

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Artificial Intelligence Is Used To Understand The Geospatial World To Improve Business And Governmental Performance - Forbes

Python and artificial intelligence are the future so learn it all here for less than $5 a course – The Next Web

TLDR: The Ultimate Python and Artificial Intelligence Certification Bundle explore training in data science and how to build machines that think for themselves.

After 20 years as one of the undisputed kings of programming languages, Java may be about to relinquish its crown. For two decades, Java and C have held the top two spots on Tiobes programming language rankings.

After experiencing what Tiobe called an all-time low in popularity, falling over 4 percentage points in year-over-year usage rates, Java is now poised to see its no. 2 rankings usurped by the hard-charging Python.

And yes, C programming should be looking over its shoulder as well. Python and its monumental role in advanced programming technologies like machine learning and artificial intelligence have made it the fastest-growing coding discipline of the past decade.

You can learn Python from the ground up as well as some of its most important applications in The Ultimate Python and Artificial Intelligence Certification Bundle. Its now available for $39.96, over 90 percent off, from TNW Deals.

This package includes nine courses featuring almost 40 hours of training covering all things Python, from basic fundamentals through to how its used in some of the most in-demand tech fields working today.

Three courses Python: Introduction to Data Science and Machine Learning A-Z, Python for Beginners: Learn All the Basics of Python and Python For Beginners: The Basics For Python Development get the training underway with basic math concepts, data science introductions, programming dos and donts, as well as everything a new user needs to understand how and why Python works so well.

After a brief segue into a pair of courses centered around data organization and visualization using fellow data science stalwart R programming, the training then steps up to more advanced Python-related subjects: deep learning and the creation of artificial intelligence.

Keras Bootcamp for Deep Learning and AI in Python gives learners a grounding in using Keras, Googles powerful deep learning framework, to create artificial neural networks and the foundations of how machines are being constructed to think and act on their own. That learning expands in Image Processing and Analysis Bootcamp with OpenCV and Deep Learning in Python, where Python Tensorflow and Keras are used to help machines actually interpret images and extract meaning.

Deep learning models get deeper exploration in Master PyTorch for Artificial Neural Networks (ANN) and Deep Learning before learning how to speed up those processes by using H2O in Artificial Intelligence (AI) in Python: A H2O Approach.

The entire package is a nearly $1,800 collection of training, but by getting in on this bundle now, you can get each course at less than $5 each, only $39.99.

Prices are subject to change.

Read next: This highly rated Google Play Store language learning app is now on sale

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Python and artificial intelligence are the future so learn it all here for less than $5 a course - The Next Web

5 Emerging AI And Machine Learning Trends To Watch In 2021 – CRN: Technology news for channel partners and solution providers

Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and smart personal assistants.

Revenue generated by AI hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019.

But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of AI and ML technologies. As we approach the end of a turbulent 2020, heres a big-picture look at five key AI and machine learning trends not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used.

The Growing Role Of AI And Machine Learning In Hyperautomation

Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated such as legacy business processes should be automated. The pandemic has accelerated adoption of the concept, which is also known as digital process automation and intelligent process automation.

AI and machine learning are key components and major drivers of hyperautomation (along with other technologies like robot process automation tools). To be successful hyperautomation initiatives cannot rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations.

Thats where AI, machine learning models and deep learning technology come in, using learning algorithms and models, along with data generated by the automated system, to allow the system to automatically improve over time and respond to changing business processes and requirements. (Deep learning is a subset of machine learning that utilizes neural network algorithms to learn from large volumes of data.)

Bringing Discipline To AI Development Through AI Engineering

Only about 53 percent of AI projects successfully make it from prototype to full production, according to Gartner research. When trying to deploy newly developed AI systems and machine learning models, businesses and organizations often struggle with system maintainability, scalability and governance, and AI initiatives often fail to generate the hoped-for returns.

Businesses and organizations are coming to understand that a robust AI engineering strategy will improve the performance, scalability, interpretability and reliability of AI models and deliver the full value of AI investments, according to Gartners list of Top Strategic Technology Trends for 2021.

Developing a disciplined AI engineering process is key. AI engineering incorporates elements of DataOps, ModelOps and DevOps and makes AI a part of the mainstream DevOps process, rather than a set of specialized and isolated projects, according to Gartner.

Increased Use Of AI For Cybersecurity Applications

Artificial intelligence and machine learning technology is increasingly finding its way into cybersecurity systems for both corporate systems and home security.

Developers of cybersecurity systems are in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDS attacks and more. AI and machine learning technology can be employed to help identify threats, including variants of earlier threats.

AI-powered cybersecurity tools also can collect data from a companys own transactional systems, communications networks, digital activity and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity such as detecting suspicious IP addresses and potential data breaches.

AI use in home security systems today is largely limited to systems integrated with consumer video cameras and intruder alarm systems integrated with a voice assistant, according to research firm IHS Markit. But IHS says AI use will expand to create smart homes where the system learns the ways, habits and preferences of its occupants improving its ability to identify intruders.

The Intersection Of AI/ML and IoT

The Internet of Things has been a fast-growing area in recent years with market researcher Transforma Insights forecasting that the global IoT market will grow to 24.1 billion devices in 2030, generating $1.5 trillion in revenue.

The use of AI/ML is increasingly intertwined with IoT. AI, machine learning and deep learning, for example, are already being employed to make IoT devices and services smarter and more secure. But the benefits flow both ways given that AI and ML require large volumes of data to operate successfully exactly what networks of IoT sensors and devices provide.

In an industrial setting, for example, IoT networks throughout a manufacturing plant can collect operational and performance data, which is then analyzed by AI systems to improve production system performance, boost efficiency and predict when machines will require maintenance.

What some are calling Artificial Intelligence of Things: (AIoT) could redefine industrial automation.

Persistent Ethical Questions Around AI Technology

Earlier this year as protests against racial injustice were at their peak, several leading IT vendors, including Microsoft, IBM and Amazon, announced that they would limit the use of their AI-based facial recognition technology by police departments until there are federal laws regulating the technologys use, according to a Washington Post story.

That has put the spotlight on a range of ethical questions around the increasing use of artificial intelligence technology. That includes the obvious misuse of AI for deepfake misinformation efforts and for cyberattacks. But it also includes grayer areas such as the use of AI by governments and law enforcement organizations for surveillance and related activities and the use of AI by businesses for marketing and customer relationship applications.

Thats all before delving into the even deeper questions about the potential use of AI in systems that could replace human workers altogether.

A December 2019 Forbes article said the first step here is asking the necessary questions and weve begun to do that. In some applications federal regulation and legislation may be needed, as with the use of AI technology for law enforcement.

In business, Gartner recommends the creation of external AI ethics boards to prevent AI dangers that could jeopardize a companys brand, draw regulatory actions or lead to boycotts or destroy business value. Such a board, including representatives of a companys customers, can provide guidance about the potential impact of AI development projects and improve transparency and accountability around AI projects.

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5 Emerging AI And Machine Learning Trends To Watch In 2021 - CRN: Technology news for channel partners and solution providers

Is the buzz around artificial intelligence justified? – Consultancy.uk

Two-thirds of senior executives believe that AI is important for the future of their business, but the average return on all AI investments by company is still struggling to pass 1%. A new survey of more than 1,000 firms has warned that patience may be key to AI success, revealing that the majority of AI change programmes take more than two years to see a return on investment.

As the global economy faces headwinds from increasing import costs, trade wars and digital disruption as well as the Covid-19 pandemic many have been investing in artificial intelligence (AI) to help them to adapt to the difficult environment. Billed as a major opportunity in which employees can be redeployed from repetitive work to value adding activities, AI has also been said for years to be able to massively improve administrative accuracy, while reducing its costs.

According to analysis by Fortune Business Insights, the global AImarket sizeis booming thanks to this hype, and was valued at $27.23 billion in 2019 and is projected to reach $266.92 billion by 2027, exhibiting a CAGR of 33.2% over that period.

New research from ESI ThoughtLab has cautioned executives against treating AI as a magic bullet to all their woes, however, and suggested that returns on investment usually take much longer to materialise than the average business leader might like to admit.

According to an examination of AI best practices, investment plans, and performance metrics of 1,200 firms, the majority of firms are posting positive returns on all AI areas. The area generating positive ROI for the largest percentage of companies is customer service, with 74% of respondents saying so, followed by IT operations (69%), and strategic planning (66%). With that being said, however, investing in AI is not a cure all.

ESI ThoughtLab said that 40% of projects are not yet showing positive ROI, based on an average ROI across all 19 areas. In fact, many firms advanced in implementing AI have yet to see positive gains. Underperforming areas include sales and business development, at 49%, and finance and auditing at 47%. The researchers suggested that this may be because businesses are underestimating how important the human side of digital change is.

Of the top performing firms in applying AI, 83% said they had been successful at developing, as opposed to just 9% of underperformers. In addition, overperformers were much better at training and enabling non-data-scientists to deploy AI, with 88% doing so, against 2% of underachievers. Illustrating how important this is in successful AI deployment, 61% of overperformers had decentralised their internal AI staff in some way, to help build AI teamwork across the firm, compared to just 22% of underperformers.

Even with the right approach, however, ESI ThoughtLab found that returns on AI investment do not always become pronounced quickly. While about two-thirds of senior executives believe that AI is important for the future of their business, the average return on all AI investments by company is still only 1.3%. Even the average return of overperformers of 4.3% pales against returns on other corporate investments, begging the question in some quarters as to whether the buzz around AI is still justified.

According to the researchers, the answer to this is still a resounding yes it is, but businesses will need to be patient when waiting to exit the payback period. An average of all firms suggests that the more familiar firms are with AI, the quicker it will pay off beginners on average face payback phases of more than 1.6 years, while leaders will see this shorten to 1.2 years however, this also depends on which industry an organisation resides in. For example, healthcare entities face the longest wait of 1.61 years, while the automotive sector averages a payback period of 1.26 years.

Commenting on the findings, ESI ThoughtLab CEO Lou Celi encouraged firms not to lose patience, as AI will become even more important in the coming months. Celi added, As the pandemic propels businesses into a digital-first world, AI will become a key driver of corporate growth and competitiveness. But building proficiency in AI is not easy It can fail to deliver results if the wrong business case is selected, the data is prepared incorrectly, or the model is not built for scale.

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Is the buzz around artificial intelligence justified? - Consultancy.uk

Artificial Intelligence Will Soon Shape Themselves, and Us – Medium

Image: Yuichiro Chino/Getty Images

A future where were all replaced by artificial intelligence may be further off than experts currently predict, but the readiness with which we accept the notion of our own obsolescence says a lot about how much we value ourselves. The long-term danger is not that we will lose our jobs to robots. We can contend with joblessness if it happens. The real threat is that well lose our humanity to the value system we embed in our robots, and that they in turn impose on us.

Computer scientists once dreamed of enhancing the human mind through technology, a field of research known as intelligence augmentation. But this pursuit has been largely surrendered to the goal of creating artificial intelligence machines that can think for themselves. All were really training them to do is manipulate our behavior and engineer our compliance. Figure has again become ground.

We shape our technologies at the moment of conception, but from that point forward they shape us. We humans designed the telephone, but from then on the telephone influenced how we communicated, conducted business, and conceived of the world. We also invented the automobile, but then rebuilt our cities around automotive travel and our geopolitics around fossil fuels. While this axiom may be true for technologies from the pencil to the birth control pill, artificial intelligences add another twist: After we launch them, they not only shape us but they also begin to shape themselves. We give them an initial goal, then give them all the data they need to figure out how to accomplish it. From that point forward, we humans no longer fully understand how an A.I. may be processing information or modifying its tactics. The A.I. isnt conscious enough to tell us. Its just trying everything, and hanging on to what works.

Researchers have found, for example, that the algorithms running social media platforms tend to show people pictures of their ex-lovers having fun. No, users dont want to see such images. But, through trial and error, the algorithms have discovered that showing us pictures of our exes having fun increases our engagement. We are drawn to click on those pictures and see what our exes are up to, and were more likely to do it if were jealous that theyve found a new partner. The algorithms dont know why this works, and they dont care. Theyre only trying to maximize whichever metric weve instructed them to pursue. Thats why the original commands we give them are so important. Whatever values we embed efficiency, growth, security, compliance will be the values A.I.s achieve, by whatever means happen to work. A.I.s will be using techniques that no one not even they understand. And they will be honing them to generate better results, and then using those results to iterate further.

We already employ A.I. systems to evaluate teacher performance, mortgage applications, and criminal records, and they make decisions just as racist and prejudicial as the humans whose decisions they were fed. But the criteria and processes they use are deemed too commercially sensitive to be revealed, so we cannot open the black box and analyze how to solve the bias. Those judged unfavorably by an algorithm have no means to appeal the decision or learn the reasoning behind their rejection. Many companies couldnt ascertain their own A.I.s criteria anyway.

As A.I.s pursue their programmed goals, they will learn to leverage human values as exploits. As they have already discovered, the more they can trigger our social instincts and tug on our heartstrings, the more likely we are to engage with them as if they were human. Would you disobey an A.I. that feels like your parent, or disconnect one that seems like your child?

Eerily echoing the rationale behind corporate personhood, some computer scientists are already arguing that A.I.s should be granted the rights of living beings rather than being treated as mere instruments or slaves. Our science fiction movies depict races of robots taking revenge on their human overlords as if this problem is somehow more relevant than the unacknowledged legacy of slavery still driving racism in America, or the 21st-century slavery on which todays technological infrastructure depends.

We are moving into a world where we care less about how other people regard us than how A.I.s do.

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Artificial Intelligence Will Soon Shape Themselves, and Us - Medium

Global Artificial Intelligence of Things Technology and Solutions Markets 2020-2025 – ResearchAndMarkets.com – Yahoo Finance

The "Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2020 through 2025. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS managed service offerings. More specifically, we see 2020 as a key year for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy services industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

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The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $4B by 2025. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Select Report Findings:

The global AIoT market will reach $65.9B by 2025, growing at 39.1% CAGR

The global market for IoT data as service solutions will reach $8.2B USD by 2025

The AI-enabled edge device market will be the fastest-growing segment within the AIoT

AIoT automates data processing systems, converting raw IoT data into useful information

Today's AIoT solutions are the precursor to next-generation AI Decision as a Service (AIDaaS)

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Defining AIoT

2.2 AI in IoT vs. AIoT

2.3 Artificial General Intelligence

2.4 IoT Network and Functional Structure

2.5 Ambient Intelligence and Smart Lifestyles

2.6 Economic and Social Impact

2.7 Enterprise Adoption and Investment

2.8 Market Drivers and Opportunities

2.9 Market Restraints and Challenges

2.10 AIoT Value Chain

2.10.1 Device Manufacturers

2.10.2 Equipment Manufacturers

2.10.3 Platform Providers

2.10.4 Software and Service Providers

2.10.5 User Communities

3.0 AIoT Technology and Market

3.1 AIoT Market

3.1.1 Equipment and Component

3.1.2 Cloud Equipment and Deployment

3.1.3 3D Sensing Technology

3.1.4 Software and Data Analytics

3.1.5 AIoT Platforms

3.1.6 Deployment and Services

3.2 AIoT Sub-Markets

3.2.1 Supporting Device and Connected Objects

3.2.2 IoT Data as a Service

3.2.3 AI Decisions as a Service

3.2.4 APIs and Interoperability

3.2.5 Smart Objects

3.2.6 Smart City Considerations

3.2.7 Industrial Transformation

3.2.8 Cognitive Computing and Computer Vision

3.2.9 Consumer Appliances

3.2.10 Domain Specific Network Considerations

3.2.11 3D Sensing Applications

3.2.12 Predictive 3D Design

3.3 AIoT Supporting Technologies

3.3.1 Cognitive Computing

3.3.2 Computer Vision

3.3.3 Machine Learning Capabilities and APIs

3.3.4 Neural Networks

3.3.5 Context-Aware Processing

3.4 AIoT Enabling Technologies and Solutions

3.4.1 Edge Computing

3.4.2 Blockchain Networks

3.4.3 Cloud Technologies

3.4.4 5G Technologies

3.4.5 Digital Twin Technology and Solutions

3.4.6 Smart Machines

3.4.7 Cloud Robotics

3.4.8 Predictive Analytics and Real-Time Processing

3.4.9 Post Event Processing

3.4.10 Haptic Technology

4.0 AIoT Applications Analysis

4.1 Device Accessibility and Security

4.2 Gesture Control and Facial Recognition

4.3 Home Automation

4.4 Wearable Device

4.5 Fleet Management

4.6 Intelligent Robots

4.7 Augmented Reality Market

4.8 Drone Traffic Monitoring

4.9 Real-time Public Safety

4.10 Yield Monitoring and Soil Monitoring Market

4.11 HCM Operation

5.0 Analysis of Important AIoT Companies

5.1 Sharp

5.2 SAS

5.3 DT42

5.4 Chania Tech Giants: Baidu, Alibaba, and Tencent

5.4.1 Baidu

5.4.2 Alibaba

5.4.3 Tencent

5.5 Xiaomi Technology

5.6 NVidia

5.7 Intel Corporation

5.8 Qualcomm

5.9 Innodisk

5.10 Gopher Protocol

5.11 Micron Technology

5.12 ShiftPixy

5.13 Uptake

Link:
Global Artificial Intelligence of Things Technology and Solutions Markets 2020-2025 - ResearchAndMarkets.com - Yahoo Finance

Global Artificial Intelligence of Things Markets 2020-2025: Focus on Technology & Solutions – AIoT Solutions Improve Operational Effectiveness and…

Dublin, Oct. 22, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2020 through 2025. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS managed service offerings. More specifically, we see 2020 as a key year for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy services industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $4B by 2025. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Select Report Findings:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Defining AIoT2.2 AI in IoT vs. AIoT2.3 Artificial General Intelligence2.4 IoT Network and Functional Structure2.5 Ambient Intelligence and Smart Lifestyles2.6 Economic and Social Impact2.7 Enterprise Adoption and Investment2.8 Market Drivers and Opportunities2.9 Market Restraints and Challenges2.10 AIoT Value Chain2.10.1 Device Manufacturers2.10.2 Equipment Manufacturers2.10.3 Platform Providers2.10.4 Software and Service Providers2.10.5 User Communities

3.0 AIoT Technology and Market3.1 AIoT Market3.1.1 Equipment and Component3.1.2 Cloud Equipment and Deployment3.1.3 3D Sensing Technology3.1.4 Software and Data Analytics3.1.5 AIoT Platforms3.1.6 Deployment and Services3.2 AIoT Sub-Markets3.2.1 Supporting Device and Connected Objects3.2.2 IoT Data as a Service3.2.3 AI Decisions as a Service3.2.4 APIs and Interoperability3.2.5 Smart Objects3.2.6 Smart City Considerations3.2.7 Industrial Transformation3.2.8 Cognitive Computing and Computer Vision3.2.9 Consumer Appliances3.2.10 Domain Specific Network Considerations3.2.11 3D Sensing Applications3.2.12 Predictive 3D Design3.3 AIoT Supporting Technologies3.3.1 Cognitive Computing3.3.2 Computer Vision3.3.3 Machine Learning Capabilities and APIs3.3.4 Neural Networks3.3.5 Context-Aware Processing3.4 AIoT Enabling Technologies and Solutions3.4.1 Edge Computing3.4.2 Blockchain Networks3.4.3 Cloud Technologies3.4.4 5G Technologies3.4.5 Digital Twin Technology and Solutions3.4.6 Smart Machines3.4.7 Cloud Robotics3.4.8 Predictive Analytics and Real-Time Processing3.4.9 Post Event Processing3.4.10 Haptic Technology

4.0 AIoT Applications Analysis4.1 Device Accessibility and Security4.2 Gesture Control and Facial Recognition4.3 Home Automation4.4 Wearable Device4.5 Fleet Management4.6 Intelligent Robots4.7 Augmented Reality Market4.8 Drone Traffic Monitoring4.9 Real-time Public Safety4.10 Yield Monitoring and Soil Monitoring Market4.11 HCM Operation

5.0 Analysis of Important AIoT Companies5.1 Sharp5.2 SAS5.3 DT425.4 Chania Tech Giants: Baidu, Alibaba, and Tencent5.4.1 Baidu5.4.2 Alibaba5.4.3 Tencent5.5 Xiaomi Technology5.6 NVidia5.7 Intel Corporation5.8 Qualcomm5.9 Innodisk5.10 Gopher Protocol5.11 Micron Technology5.12 ShiftPixy5.13 Uptake5.14 C3 IoT5.15 Alluvium5.16 Arundo Analytics5.17 Canvass Analytics5.18 Falkonry5.19 Interactor5.20 Google5.21 Cisco5.22 IBM Corp.5.23 Microsoft Corp.5.24 Apple Inc.5.25 Salesforce Inc.5.26 Infineon Technologies AG5.27 Amazon Inc.5.28 AB Electrolux5.29 ABB Ltd.5.30 AIBrian Inc.5.31 Analog Devices5.32 ARM Limited5.33 Atmel Corporation5.34 Ayla Networks Inc.5.35 Brighterion Inc.5.36 Buddy5.37 CloudMinds5.38 Cumulocity GmBH5.39 Cypress Semiconductor Corp5.40 Digital Reasoning Systems Inc.5.41 Echelon Corporation5.42 Enea AB5.43 Express Logic Inc.5.44 Facebook Inc.5.45 Fujitsu Ltd.5.46 Gemalto N.V.5.47 General Electric5.48 General Vision Inc.5.49 Graphcore5.50 H2O.ai5.51 Haier Group Corporation5.52 Helium Systems5.53 Hewlett Packard Enterprise5.54 Huawei Technologies5.55 Siemens AG5.56 SK Telecom5.57 SoftBank Robotics5.58 SpaceX5.59 SparkCognition5.60 STMicroelectronics5.61 Symantec Corporation5.62 Tellmeplus5.63 Tend.ai5.64 Tesla5.65 Texas Instruments5.66 Thethings.io5.67 Veros Systems5.68 Whirlpool Corporation5.69 Wind River Systems5.70 Juniper Networks5.71 Nokia Corporation5.72 Oracle Corporation5.73 PTC Corporation5.74 Losant IoT5.75 Robert Bosch GmbH5.76 Pepper5.77 Terminus5.78 Tuya Smart

6.0 AIoT Market Analysis and Forecasts 2020 - 20256.1 Global AIoT Market Outlook and Forecasts6.1.1 Aggregate AIoT Market 2020 - 20256.1.2 AIoT Market by Infrastructure and Services 2020 - 20256.1.3 AIoT Market by AI Technology 2020 - 20256.1.4 AIoT Market by Application 2020 - 20256.1.5 AIoT in Consumer, Enterprise, Industrial, and Government 2020 - 20256.1.6 AIoT Market in Cities, Suburbs, and Rural Areas 2020 - 20256.1.7 AIoT in Smart Cities 2020 - 20256.1.8 IoT Data as a Service Market 2020 - 20256.1.9 AI Decisions as a Service Market 2020 - 20256.1.10 Blockchain Support of AIoT 2020 - 20256.1.11 AIoT in 5G Networks 2020 - 20256.2 Regional AIoT Markets 2020 - 2025

7.0 Conclusions and Recommendations7.1 Advertisers and Media Companies7.2 Artificial Intelligence Providers7.3 Automotive Companies7.4 Broadband Infrastructure Providers7.5 Communication Service Providers7.6 Computing Companies7.7 Data Analytics Providers7.8 Immersive Technology (AR, VR, and MR) Providers7.9 Networking Equipment Providers7.10 Networking Security Providers7.11 Semiconductor Companies7.12 IoT Suppliers and Service Providers7.13 Software Providers7.14 Smart City System Integrators7.15 Automation System Providers7.16 Social Media Companies7.17 Workplace Solution Providers7.18 Enterprise and Government

For more information about this report visit https://www.researchandmarkets.com/r/aw2mh9

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Global Artificial Intelligence of Things Markets 2020-2025: Focus on Technology & Solutions - AIoT Solutions Improve Operational Effectiveness and...